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使用CNN在MNIST上实现简单的攻击样本https://github.com/wmn7/ML_Practice/blob/master/2019_06_03/CNN_MNIST%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb 文章目录 在 MNIST 上实现简单的攻击样本1 训练一个数字分类网络2 控制输出的概率, 看输入是什么3 让正确的图片分…
学习参考来自
使用CNN在MNIST上实现简单的攻击样本https://github.com/wmn7/ML_Practice/blob/master/2019_06_03/CNN_MNIST%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb 文章目录 在 MNIST 上实现简单的攻击样本1 训练一个数字分类网络2 控制输出的概率, 看输入是什么3 让正确的图片分类错误 在 MNIST 上实现简单的攻击样本
要看某个filter在检测什么我们让通过其后的输出激活值较大。
想要一些攻击样本有一个简单的想法就是让最后结果中是某一类的概率值变大接着进行反向传播(固定住网络的参数)去修改input
原理图 1 训练一个数字分类网络
模型基础数据、超参数的配置
import time
import csv, osimport PIL.Image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
from cv2 import resizeimport torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data.sampler import SubsetRandomSampler
from torch.autograd import Variableimport copy
import torchvision
import torchvision.transforms as transforms
os.environ[CUDA_VISIBLE_DEVICES] 1# --------------------
# Device configuration
# --------------------
device torch.device(cuda if torch.cuda.is_available() else cpu)# ----------------
# Hyper-parameters
# ----------------
num_classes 10
num_epochs 3
batch_size 100
validation_split 0.05 # 每次训练集中选出10%作为验证集
learning_rate 0.001# -------------
# MNIST dataset
# -------------
train_dataset torchvision.datasets.MNIST(root./,trainTrue,transformtransforms.ToTensor(),downloadTrue)test_dataset torchvision.datasets.MNIST(root./,trainFalse,transformtransforms.ToTensor())
# -----------
# Data loader
# -----------
test_len len(test_dataset) # 计算测试集的个数 10000
test_loader torch.utils.data.DataLoader(datasettest_dataset,batch_sizebatch_size,shuffleFalse)for (inputs, labels) in test_loader:print(inputs.size()) # [100, 1, 28, 28]print(labels.size()) # [100]break# ------------------
# 下面切分validation
# ------------------
dataset_len len(train_dataset) # 60000
indices list(range(dataset_len))
# Randomly splitting indices:
val_len int(np.floor(validation_split * dataset_len)) # validation的长度
validation_idx np.random.choice(indices, sizeval_len, replaceFalse) # validatiuon的index
train_idx list(set(indices) - set(validation_idx)) # train的index
## Defining the samplers for each phase based on the random indices:
train_sampler SubsetRandomSampler(train_idx)
validation_sampler SubsetRandomSampler(validation_idx)
train_loader torch.utils.data.DataLoader(datasettrain_dataset,samplertrain_sampler,batch_sizebatch_size)
validation_loader torch.utils.data.DataLoader(train_dataset,samplervalidation_sampler,batch_sizebatch_size)
train_dataloaders {train: train_loader, val: validation_loader} # 使用字典的方式进行保存
train_datalengths {train: len(train_idx), val: val_len} # 保存train和validation的长度搭建简单的神经网络
# -------------------------------------------------------
# Convolutional neural network (two convolutional layers)
# -------------------------------------------------------
class ConvNet(nn.Module):def __init__(self, num_classes10):super(ConvNet, self).__init__()self.layer1 nn.Sequential(nn.Conv2d(in_channels1, out_channels16, kernel_size5, stride1, padding2),nn.BatchNorm2d(16),nn.ReLU(),nn.MaxPool2d(kernel_size2, stride2))self.layer2 nn.Sequential(nn.Conv2d(in_channels16, out_channels32, kernel_size5, stride1, padding2),nn.BatchNorm2d(32),nn.ReLU(),nn.MaxPool2d(kernel_size2, stride2))self.fc nn.Linear(7*7*32, num_classes)def forward(self, x):out self.layer1(x)out self.layer2(out)out out.reshape(out.size(0), -1)out self.fc(out)return out绘制损失和精度变化曲线
# -----------
# draw losses
# -----------
def draw_loss_acc(train_list, validation_list, modeloss):plt.style.use(seaborn)# set interdata_len len(train_list)x_ticks np.arange(1, data_len 1)plt.xticks(x_ticks)if mode Loss:plt.plot(x_ticks, train_list, labelTrain Loss)plt.plot(x_ticks, validation_list, labelValidation Loss)plt.xlabel(Epoch)plt.ylabel(Loss)plt.legend()plt.savefig(Epoch_loss.jpg)elif mode Accuracy:plt.plot(x_ticks, train_list, labelTrain AccuracyAccuracy)plt.plot(x_ticks, validation_list, labelValidation Accuracy)plt.xlabel(Epoch)plt.ylabel(Accuracy)plt.legend()plt.savefig(Epoch_Accuracy.jpg)定义训练模型的函数
# ---------------
# Train the model
# ---------------
def train_model(model, criterion, optimizer, dataloaders, train_datalengths, schedulerNone, num_epochs2):传入的参数分别是:1. model:定义的模型结构2. criterion:损失函数3. optimizer:优化器4. dataloaders:training dataset5. train_datalengths:train set和validation set的大小, 为了计算准确率6. scheduler:lr的更新策略7. num_epochs:训练的epochssince time.time()# 保存最好一次的模型参数和最好的准确率best_model_wts copy.deepcopy(model.state_dict())best_acc 0.0train_loss [] # 记录每一个epoch后的train的losstrain_acc []validation_loss []validation_acc []for epoch in range(num_epochs):print(Epoch [{}/{}].format(epoch 1, num_epochs))print(- * 10)# Each epoch has a training and validation phasefor phase in [train, val]:if phase train:if scheduler ! None:scheduler.step()model.train() # Set model to training modeelse:model.eval() # Set model to evaluate moderunning_loss 0.0 # 这个是一个epoch积累一次running_corrects 0 # 这个是一个epoch积累一次# Iterate over data.total_step len(dataloaders[phase])for i, (inputs, labels) in enumerate(dataloaders[phase]):# inputs inputs.reshape(-1, 28*28).to(device)inputs inputs.to(device)labels labels.to(device)# zero the parameter gradientsoptimizer.zero_grad()# forward# track history if only in trainwith torch.set_grad_enabled(phase train):outputs model(inputs)_, preds torch.max(outputs, 1) # 使用output(概率)得到预测loss criterion(outputs, labels) # 使用output计算误差# backward optimize only if in training phaseif phase train:loss.backward()optimizer.step()# statisticsrunning_loss loss.item() * inputs.size(0)running_corrects torch.sum(preds labels.data)if (i 1) % 100 0:# 这里相当于是i*batch_size的样本个数打印一次, i*100iteration_loss loss.item() / inputs.size(0)iteration_acc 100 * torch.sum(preds labels.data).item() / len(preds)print(Mode {}, Epoch [{}/{}], Step [{}/{}], Accuracy: {}, Loss: {:.4f}.format(phase, epoch 1,num_epochs, i 1,total_step,iteration_acc,iteration_loss))epoch_loss running_loss / train_datalengths[phase]epoch_acc running_corrects.double() / train_datalengths[phase]if phase train:train_loss.append(epoch_loss)train_acc.append(epoch_acc)else:validation_loss.append(epoch_loss)validation_acc.append(epoch_acc)print(* * 10)print(Mode: [{}], Loss: {:.4f}, Acc: {:.4f}.format(phase, epoch_loss, epoch_acc))print(* * 10)# deep copy the modelif phase val and epoch_acc best_acc:best_acc epoch_accbest_model_wts copy.deepcopy(model.state_dict())print()time_elapsed time.time() - sinceprint(* * 10)print(Training complete in {:.0f}m {:.0f}s.format(time_elapsed // 60, time_elapsed % 60))print(Best val Acc: {:4f}.format(best_acc))print(* * 10)# load best model weightsfinal_model copy.deepcopy(model) # 最后得到的modelmodel.load_state_dict(best_model_wts) # 在验证集上最好的modeldraw_loss_acc(train_listtrain_loss, validation_listvalidation_loss, modeLoss) # 绘制Loss图像draw_loss_acc(train_listtrain_acc, validation_listvalidation_acc, modeAccuracy) # 绘制准确率图像return (model, final_model)开始训练并保存模型
if __name__ __main__:# 模型初始化model ConvNet(num_classesnum_classes).to(device)# 打印模型结构#print(model)ConvNet((layer1): Sequential((0): Conv2d(1, 16, kernel_size(5, 5), stride(1, 1), padding(2, 2))(1): BatchNorm2d(16, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU()(3): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse))(layer2): Sequential((0): Conv2d(16, 32, kernel_size(5, 5), stride(1, 1), padding(2, 2))(1): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)(2): ReLU()(3): MaxPool2d(kernel_size2, stride2, padding0, dilation1, ceil_modeFalse))(fc): Linear(in_features1568, out_features10, biasTrue))# -------------------# Loss and optimizer# ------------------criterion nn.CrossEntropyLoss()optimizer torch.optim.Adam(model.parameters(), lrlearning_rate)# -------------# 进行模型的训练# -------------(best_model, final_model) train_model(modelmodel, criterioncriterion, optimizeroptimizer,dataloaderstrain_dataloaders, train_datalengthstrain_datalengths,num_epochsnum_epochs)Epoch [1/3]----------Mode train, Epoch [1/3], Step [100/570], Accuracy: 96.0, Loss: 0.0012Mode train, Epoch [1/3], Step [200/570], Accuracy: 96.0, Loss: 0.0011Mode train, Epoch [1/3], Step [300/570], Accuracy: 100.0, Loss: 0.0004Mode train, Epoch [1/3], Step [400/570], Accuracy: 100.0, Loss: 0.0003Mode train, Epoch [1/3], Step [500/570], Accuracy: 96.0, Loss: 0.0010**********Mode: [train], Loss: 0.1472, Acc: 0.9579********************Mode: [val], Loss: 0.0653, Acc: 0.9810**********Epoch [2/3]----------Mode train, Epoch [2/3], Step [100/570], Accuracy: 99.0, Loss: 0.0005Mode train, Epoch [2/3], Step [200/570], Accuracy: 98.0, Loss: 0.0003Mode train, Epoch [2/3], Step [300/570], Accuracy: 98.0, Loss: 0.0003Mode train, Epoch [2/3], Step [400/570], Accuracy: 97.0, Loss: 0.0005Mode train, Epoch [2/3], Step [500/570], Accuracy: 98.0, Loss: 0.0004**********Mode: [train], Loss: 0.0470, Acc: 0.9853********************Mode: [val], Loss: 0.0411, Acc: 0.9890**********Epoch [3/3]----------Mode train, Epoch [3/3], Step [100/570], Accuracy: 99.0, Loss: 0.0006Mode train, Epoch [3/3], Step [200/570], Accuracy: 99.0, Loss: 0.0009Mode train, Epoch [3/3], Step [300/570], Accuracy: 98.0, Loss: 0.0004Mode train, Epoch [3/3], Step [400/570], Accuracy: 99.0, Loss: 0.0003Mode train, Epoch [3/3], Step [500/570], Accuracy: 100.0, Loss: 0.0002**********Mode: [train], Loss: 0.0348, Acc: 0.9890********************Mode: [val], Loss: 0.0432, Acc: 0.9867********************Training complete in 0m 32sBest val Acc: 0.989000**********torch.save(model, CNN_MNIST.pkl)训练完的 loss 和 acc 曲线 载入模型进行简单的测试以下面这张 7 为例子
model torch.load(CNN_MNIST.pkl)
# print(test_dataset.data[0].shape) # torch.Size([28, 28])
# print(test_dataset.targets[0]) # tensor(7)
#
unload transforms.ToPILImage()
img unload(test_dataset.data[0])
img.save(test_data_0.jpg)# 带入模型进行预测
inputdata test_dataset.data[0].view(1,1,28,28).float()/255
inputdata inputdata.to(device)
outputs model(inputdata)
print(outputs)tensor([[ -7.4075, -3.4618, -1.5322, 1.1591, -10.6026, -5.5818, -17.4020,14.6429, -4.5378, 1.0360]], devicecuda:0,grad_fnAddmmBackward0)print(torch.max(outputs, 1))torch.return_types.max(
valuestensor([14.6429], devicecuda:0, grad_fnMaxBackward0),
indicestensor([7], devicecuda:0))OK预测结果正常为 7
2 控制输出的概率, 看输入是什么
步骤
初始随机一张图片我们希望让分类中某个数的概率最大最后看一下这个网络认为什么样的图像是这个数字
# hook类的写法
class SaveFeatures():注册hook和移除hookdef __init__(self, module):self.hook module.register_forward_hook(self.hook_fn)def hook_fn(self, module, input, output):# self.features output.clone().detach().requires_grad_(True)self.features output.clone()def close(self):self.hook.remove()下面开始操作
# hook住模型
layer 2
activations SaveFeatures(list(model.children())[layer])# 超参数
lr 0.005 # 学习率
opt_steps 100 # 迭代次数
upscaling_factor 10 # 放大的倍数(为了最后图像的保存)# 保存迭代后的数字
true_nums []
random_init, num_init 1, 0 # the type of initial# 带入网络进行迭代
for true_num in range(0, 10):# 初始化随机图片(数据定义和优化器一定要在一起)# 定义数据sz 28if random_init:img np.uint8(np.random.uniform(0, 255, (1,sz, sz))) / 255img torch.from_numpy(img[None]).float().to(device)img_var Variable(img, requires_gradTrue)if num_init:# 将7变成0,1,2,3,4,5,6,7,8,9img test_dataset.data[0].view(1, 1, 28, 28).float() / 255img img.to(device)img_var Variable(img, requires_gradTrue)# 定义优化器optimizer torch.optim.Adam([img_var], lrlr, weight_decay1e-6)for n in range(opt_steps): # optimize pixel values for opt_steps timesoptimizer.zero_grad()model(img_var) # 正向传播if random_init:loss -activations.features[0, true_num] # 这里的loss确保某个值的输出大if num_init:loss -activations.features[0, true_num] F.mse_loss(img_var, img) # 这里的loss确保某个值的输出大, 并且与原图不会相差很多loss.backward()optimizer.step()# 打印最后的img的样子print(activations.features[0, true_num]) # tensor(23.8736, devicecuda:0, grad_fnSelectBackward0)print(activations.features[0])tensor([ 23.8736, -32.0724, -5.7329, -18.6501, -16.1558, -18.9483, -0.3033,-29.1561, 14.9260, -13.5412], devicecuda:0,grad_fnSelectBackward0)print()img img_var.cpu().clone()img img.squeeze(0)# 图像的裁剪(确保像素值的范围)img[img 1] 1img[img 0] 0true_nums.append(img)unloader transforms.ToPILImage()img unloader(img)img cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)sz int(upscaling_factor * sz) # calculate new image sizeimg cv2.resize(img, (sz, sz), interpolationcv2.INTER_CUBIC) # scale image upif random_init:cv2.imwrite(random_regonize{}.jpg.format(true_num), img)if num_init:cv2.imwrite(real7_regonize{}.jpg.format(true_num), img)tensor(22.3530, devicecuda:0, grad_fnSelectBackward0)tensor([-22.4025, 22.3530, 6.0451, -9.4460, -1.0577, -17.7650, -11.3686,-11.8474, -5.5310, -16.9936], devicecuda:0,grad_fnSelectBackward0)tensor(50.2202, devicecuda:0, grad_fnSelectBackward0)tensor([-16.9364, -16.5120, 50.2202, -9.5287, -25.2837, -32.3480, -22.8569,-20.1231, 1.1174, -31.7244], devicecuda:0,grad_fnSelectBackward0)tensor(48.8004, devicecuda:0, grad_fnSelectBackward0)tensor([-33.3715, -30.5732, -6.0252, 48.8004, -34.9467, -17.8136, -35.1371,-17.4484, 2.8954, -11.9694], devicecuda:0,grad_fnSelectBackward0)tensor(31.5068, devicecuda:0, grad_fnSelectBackward0)tensor([-24.5204, -13.6857, -5.1833, -22.7889, 31.5068, -20.2855, -16.7245,-19.1719, 2.4699, -16.2246], devicecuda:0,grad_fnSelectBackward0)tensor(37.4866, devicecuda:0, grad_fnSelectBackward0)tensor([-20.2235, -26.1013, -25.9511, -2.7806, -19.5546, 37.4866, -10.8689,-30.3888, 0.1591, -8.5250], devicecuda:0,grad_fnSelectBackward0)tensor(35.9310, devicecuda:0, grad_fnSelectBackward0)tensor([ -6.1996, -31.2246, -8.3396, -21.6307, -16.9098, -9.5194, 35.9310,-33.0918, 10.2462, -28.6393], devicecuda:0,grad_fnSelectBackward0)tensor(23.6772, devicecuda:0, grad_fnSelectBackward0)tensor([-21.5441, -10.3366, -4.9905, -0.9289, -6.9219, -23.5643, -23.9894,23.6772, -0.7960, -16.9556], devicecuda:0,grad_fnSelectBackward0)tensor(57.8378, devicecuda:0, grad_fnSelectBackward0)tensor([-13.5191, -53.9004, -9.2996, -10.3597, -27.3806, -27.5858, -15.3235,-46.7014, 57.8378, -17.2299], devicecuda:0,grad_fnSelectBackward0)tensor(37.0334, devicecuda:0, grad_fnSelectBackward0)tensor([-26.2983, -37.7131, -16.6210, -1.8686, -11.5330, -11.7843, -25.7539,-27.0036, 6.3785, 37.0334], devicecuda:0,grad_fnSelectBackward0)
# 移除hook
activations.close()for i in range(0,10):_ , pre torch.max(model(true_nums[i][None].to(device)),1)print(i:{},Pre:{}.format(i,pre))i:0,Pre:tensor([0], devicecuda:0)i:1,Pre:tensor([1], devicecuda:0)i:2,Pre:tensor([2], devicecuda:0)i:3,Pre:tensor([3], devicecuda:0)i:4,Pre:tensor([4], devicecuda:0)i:5,Pre:tensor([5], devicecuda:0)i:6,Pre:tensor([6], devicecuda:0)i:7,Pre:tensor([7], devicecuda:0)i:8,Pre:tensor([8], devicecuda:0)i:9,Pre:tensor([9], devicecuda:0)可以看到相应最高的图片的样子依稀可以看见 0123456789 的轮廓
3 让正确的图片分类错误
步骤如下
使用特定数字的图片如数字7(初始化方式与前面的不一样是特定而不是随机)使用上面训练好的网络固定网络参数最后一层因为是10个输出(相当于是概率), 我们 loss 设置为某个的负的概率值利于梯度下降梯度下降, 使负的概率下降即相对应的概率值上升我们最终修改的是初始的图片这样使得网络认为这张图片识别的数字的概率增加
简单描述输入图片数字7固定网络参数让其他数字的概率不断变大改变输入图片最终达到迷惑网络的目的
只要将第二节中的代码 random_init, num_init 1, 0 # the type of initial 改为 random_init, num_init 0, 1 # the type of initial 即可 成功用 7 迷惑了所有类别
注意损失函数中还额外引入了 F.mse_loss(img_var, img) 确保某个数字类别的概率输出增大, 并且与原图数字 7 像素上不会相差很多
